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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15โ20% | - Manage data sharing and permissions
|
| Prepare and process data | 30โ35% | - Optimize and manage data storage
|
| Deploy and maintain data pipelines and workloads | 30โ35% | - Monitor, troubleshoot, and maintain workloads
|
| Set up and configure an Azure Databricks environment | 15โ20% | - Select and configure compute resources
|
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NEW QUESTION # 21
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The right ingestion tool depends on the source characteristics:
File-based telemetry and maintenance data # Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.
Real-time telemetry from Event Hubs # Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying 'resume processing after failures without reprocessing.' Structured maintenance data from PostgreSQL # JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.
Daily CSV ERP extracts (50-100 GB) # COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/
NEW QUESTION # 22
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs on a schedule and processes business-critical data.
You need to ensure that you receive a notification if Job1 fails.
What should you do?
Answer: A
Explanation:
Lakeflow Jobs provides built-in job notifications for important run events, including start, successful completion, failure, duration warnings, and streaming-backlog thresholds. Configuring a failure notification for Job1 ensures that the designated email address or system destination receives a notification when a scheduled run ends unsuccessfully. This is the most direct solution and requires no separate monitoring query or alert-processing infrastructure. Notifications can be delivered through email or configured destinations such as Microsoft Teams, Slack, PagerDuty, or an HTTP webhook. Microsoft Learn A SQL alert task evaluates the results of a SQL query and is not intended to monitor the execution state of the overall job. Sending results to Log Analytics would centralize diagnostic information but would not independently notify anyone unless an additional alerting solution was configured. An Azure Monitor alert could potentially be developed, but it would add unnecessary configuration and administration when Lakeflow Jobs already supports failure notifications. Therefore, configuring a job-run failure notification is the simplest and most appropriate solution.
NEW QUESTION # 23
You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?
Answer: D
Explanation:
The correct answer is B - define a pipeline expectation.
SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline's event log as expectation violations - available for review without any extra code.
Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code.
Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views - they're not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the 'pipeline continues to run' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations
NEW QUESTION # 24
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
A catalog named Finance
A schema named Purchases in the Finance catalog
A table named Transactions in the Purchases schema
You need to ensure that a user named finance_user can query the Transactions table. The solution must follow the principle of least privilege.
Which permission should you grant to finance_user for each object? To answer, drag the appropriate permissions to the correct objects. Each permission may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The Finance catalog: USE CATALOG
The Purchases schema: USE SCHEMA
The Transactions table: SELECT
NEW QUESTION # 25
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You run the following expression.
df.dropna(subset=["order_amount"])
The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null.
* You run the following expression.
df.filter(df.order_amount.isNotNull())
To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions:
Option 1: Python/PySpark API (Recommended)
pythondf_clean = df.filter(df["order_amount"].isNotNull())
Incorrect:
* You run the following expression.
df.fillna(0, subset=['order_amount'])
* You run the following expression.
df.filter(df.order_amount != None)
Reference:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna
NEW QUESTION # 26
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